code: def _compute_shape(self, data: Tensor):
      s = data.transpose(1, 2)
      shape = []
      for i, wsize in enumerate(self.window_size):
          # left_pad + right_pad = window_size - 1
          left_pad = wsize // 2
          right_pad = wsize // 2 - (1 - wsize % 2)
          x = F.pad(
              s,
              [left_pad, right_pad],
              mode="constant",
              value=0.0,
          )
          x = F.conv1d(x, self._shape_weights[i], self._shape_biases[i])
          shape.append(x)
      shape = pt.cat(shape, dim=1)
      shape = shape.transpose(1, 2)
      return shape
  
imports: from typing import Optional, List, Tuple, Union
  import numpy
  import torch
  from torch import Tensor, BoolTensor, nn
  from torch.nn import init, functional
  from tslib.nn.transformer import PositionwiseFFN
  from .kernel import AttentionKernel
  from .disc import SimpleDiscriminator
label_desc: Use image node_id 3 for calling the EncoderModule obj's underlying member method code with example usage: obj._compute_shape(data) and returns: shape